An investigation into physicochemical characteristics of ash produced from combustion of oil palm biomass wastein a boiler

Ash derived from combustion of Malaysian oil palm biomass (empty fruit bunches consisting of fibers) was physically and chemically characterized in order to provide a comprehensive understanding of its specific properties in terms of toxicity, compositions and reusability. Principal analyses conducted include particle size distribution, scanning electron microscopy, elemental dispersive X-ray, elemental analysis, toxicity characteristic leaching procedure (TCLP) as well as thermogravimetric, X-ray diffractometry and Fourier-transform infrared analyses. TCLP result indicated that the oil palm ash (OPA) should not be classified as toxic wastes in terms of heavy metal leachability since leachable copper, cadmium, lead and nickel concentrations were detected below the stipulated leachability limits. It was determined that the OPA contained high amount of potassium as well as presence of silica which implied its suitability to be reused as crude fertilizer or cement replacement material.     

优化移动多媒体传输链的功耗

消费者对于移动多媒体的需求是毫无疑问的，满足这些需求的技术已经开发成功，对于设计人员而言，剩下的问题就是如何利用这些技术，并将成功的产品带入市场。本文将讨论如何减省移动多媒体设备内关键元件的功耗，此外也会对现今移动多媒体设备设计人员所面对的工程挑战作出概括的分析，并就目前及新涌现的硅技术，提出可克服这些工程挑战的解决方案。     

Serologic evidence of previous Campylobacter jejuni infection in patients with the Guillain-Barré syndrome

OBJECTIVE: To determine if patients with the Guillain-Barré syndrome are likely to have had Campylobacter jejuni infection before onset of neurologic symptoms. DESIGN: A case-control study. SETTING: Several university medical centers. PATIENTS: Case patients met clinical criteria for the Guillain-Barré syndrome between 1983 and 1990 and had a serum sample collected and frozen within 3 weeks after onset of neurologic symptoms (n = 118). Disease controls were patients with other neurologic illnesses (n = 56); healthy controls were hospital employees or healthy family members of patients (n = 47). MEASUREMENTS: Serum IgA, IgG, and IgM antibodies to C. jejuni were determined by enzyme-linked immunosorbent assays. Assays were done in a blinded manner. RESULTS: Optical density ratios > or = 2 in two or more immunoglobulin classes were seen in 43 (36%) of patients with the Guillain-Barré syndrome and in 10 (10%) of controls (odds ratio, 5.3; 95% CI, 2.4 to 12.5; P < 0.001). Increasing the optical density ratio or the number of immunoglobulin classes necessary to yield a positive result increased the strength of the association. The number of patients with the Guillain-Barré syndrome who had positive serologic responses was greatest from September to November (P = 0.02). Male patients were three times more likely to have serologic evidence of C. jejuni infection (P = 0.009); the proportion of patients with the syndrome who had a positive serologic response increased with age. CONCLUSIONS: Patients with the Guillain-Barré syndrome are more likely than controls to have serologic evidence of C. jejuni infection in the weeks before onset of neurologic symptoms. Campylobacter jejuni may play a role in the initiation of the Guillain-Barré syndrome in many patients.     

Investigations of surface characterization of silicone rubber due to tracking phenomena under a.c. and d.c. voltages

In the present work, tracking phenomena has been studied with silicone rubber material under the a.c. and d.c. voltages following IEC-587 standards. The surface condition of the tracked zone was analysed using wide angle X-ray diffraction (WAXD) and thermogravimetric differential thermal analysis (TG-DTA) studies. The tracking time was different for a.c. and d.c. voltages.     

Enzymic and in sacco methods to estimate rumen degradation of food protein in cattle

BACKGROUND: Two factorial studies compared enzymic and in sacco methods to estimate degradation of ruminant foods. Enzyme degradation (in vitro = enzyme) was determined from the release of leucine‐equivalent amino acid (LA) crude protein (CP) from sunflower meal (SF), maize gluten meal (MG), distillers' dark grain (DG) and field beans (FB) after their separate incubations with Streptomyces griseus enzyme for 0–24 h. In sacco crude protein (CP) degradation of these foods was estimated during washing (0 h) and rumen incubations in fistulated cows for 2–24 h. The LA data were expressed as g LA per either kg of CP (LACP) or acid‐hydrolysable LA (HLA) of each food and compared with in sacco data. RESULTS: These methods showed comparable degradation with time (P < 0.01). The in sacco and HLA were greater than LACP for all foods except MG where in sacco value was either lower or equal to LACP depending upon the incubation time (P > 0.05 or P < 0.05). Conversely, HLA was significantly (P < 0.01) greater than LACP from 2 h onwards. At 0 h, in sacco values were significantly greater than those of enzyme for SF, DG and FB (P < 0.05) but not for MG. The foods differed significantly for degradation constants (a, b, c) in each method (P < 0.05). CONCLUSIONS: Despite variations between in sacco and enzyme estimates for different foods, the relationships between these estimates suggest that the HLA enzyme method has the potential to estimate food degradation. Copyright © 2007 Society of Chemical Industry     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Modeling and control of a finger-like mechanism using bending shape memory alloys

Microsystem Technologies - In this research a biologically inspired finger-like mechanism similar to human musculoskeletal system is developed based on Shape Memory Alloys (SMAs). SMA actuators are...     

Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

Neural Computing and Applications - Cryptography often involves substituting (and converting) the secret information into dummy data so that it could reach the desired destination without leakage....     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....